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We present FedScale, a federated learning (FL) benchmarking suite withrealistic datasets and a scalable runtime to enable reproducible FL research.FedScale datasets encompass a wide range of critical FL tasks, ranging fromimage classification and object detection to language modeling and speechrecognition. Each dataset comes with a unified evaluation protocol usingreal-world data splits and evaluation metrics. To reproduce realistic FLbehavior, FedScale contains a scalable and extensible runtime. It provideshigh-level APIs to implement FL algorithms, deploy them at scale across diversehardware and software backends, and evaluate them at scale, all with minimaldeveloper efforts. We combine the two to perform systematic benchmarkingexperiments and highlight potential opportunities for heterogeneity-awareco-optimizations in FL. FedScale is open-source and actively maintained bycontributors from different institutions at http://fedscale.ai. We welcomefeedback and contributions from the community.

FedScale: Benchmarking Model and System Performance of Federated Learning at Scale